Prof. Wang received his doctor degree in computer science from Harbin Institute of Technology in September 1998. From 1998 to 2001 Prof. Wang worked at Department of Computing in Hong Kong Polytechnic University as a research fellow. From October 2000 to March 2014 Prof. Wang served in Hebei University as the Dean of school of Mathematics and Computer Sciences. From October 2007 to March 2014 Prof. Wang was the founding Director of Key Lab. on Machine Learning and Computational Intelligence in Hebei Province, China. From September to November in 2013, Prof. Wang was a visiting professor of Canada Simon Fraser University. From December 2013 to January 2014, Prof. Wang was a visiting professor of University of Alberta in Canada. From July to September in 2014, Prof. Wang was a visiting professor of Australia New South Wales University at Canberra. Since March 2014 to now Prof. Wang has moved to college of computer science and software engineering in Shenzhen University as a professor and a director of Big Data Institute.

Prof. Wang's main research interest is machine learning and uncertainty information processing including inductive learning with fuzzy representation, approximate reasoning and expert systems, neural networks and their sensitivity analysis, statistical learning theory, fuzzy measures and fuzzy integrals, random weight network, and the recent topic: machine learning theories and methodologies in Big-Data environment. The main research feature is, through discovering and representing the uncertainty hidden in big data, to dig the distribution of big data and then use distributed parallel technology to design and implement classification and clustering algorithms which are suitable for different types of big data. It focuses on the corresponding key issues of theory and technology of big data analytics.

Academic contributions: (1) Putting forward the concept of "fuzzy learning from examples" for the first time in 1996 during his PhD thesis, and extending machine learning approaches into the uncertainty framework. His research in this aspect lasted almost 20 years and acquired a series of achievements with significant impact, for example, the project “fuzzy-valued attribute feature subset selection” won the first prize of Hebei province natural science in 2007. (2) Establishing a refinement methodology and technique for similarity based clustering, called departure-0,5, and extending it to a new branch of semi-supervised learning based on departure-0.5, and further applying successfully to the big data learning. Mainly due to this contribution Prof. Wang was elected as an IEEE Fellow in 2012. (3). Proposing the viewpoint that uncertainty modeling and its effective handling play a crucial/indispensible role in improving the generalization ability for a big data learning system. The view point is highly recognized by the experts in related domains, and is funded by a NSFC key project (Uncertainty modeling in learning from big data, 2018-2022).

Research achievements: Prof. Wang has published 3 monographs and 2 textbooks. He has also published 200+ research papers in famous magazine and conferences in the field of machine learning and uncertainty, among which 150+ publications have been included in SCI or EI databases. The journals include IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, Machine Learning, Information Sciences and Fuzzy Sets and Systems. By Google scholar in November 2017, the total number of citations is 6360, the maximum number of citations for a single paper is 600, and the SCI-H index is 42. Prof. Wang has completed 30+ research projects including ones funded by National Natural Sciences Fund of China, by Ministry of Education, by National Development and Reform Commission, by Hebei Province Natural Science, and by RGC in Hong Kong.

Awards and honors: Prof. Wang has received the First-Class Award of Natural Science Advances of Hebei Province and the Second-Class Award of Natural Science of Education Ministry in 2007. He was selected as one member of the first hundred of excellent innovative talents of Hebei province in 2007. He gained the honor of Model Teacher of China in 2009. Prof. Wang was evaluated as an IEEE Fellow in 2012 and a CAAI Fellow in 2017. He was chosen as the local leading talent of Shenzhen in 2013 and one of the Chinese scholars whose academic papers have been highly cited based on Elsevier statistics in 2014/15/16. Prof. Wang was identified as the overseas high-level (peacock B class) talent of Shenzhen in 2015.

Education

1998.09

PhD in Computer Science from Harbin Institute of Technology

1995.09-1996.07

PhD candidate at Dept. of Computer Science in Harbin Institute of Technology

1990.02

Master Degree in Mathematics from Hebei University

1985.09-1987.07

Postgraduate at Department of Mathematics in Shanghai Jiaotong University

Xizhao Wang(*) ,Xianghui Gao, A research on the relation between training ambiguity and generalization capability, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 August 2006, Proceedings pp: 2008-2013.（EI）

[40]

Miao Wang(*), Xizhao Wang, A research on weight acquisition of weighted fuzzy production rules based on genetic algorithm, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 August 2006, Proceedings pp: 2208-2211.（EI）

Xizhao Wang(*), Mingzhu Lu and Jianbing Huo, Fault diagnosis of power transformer based on large margin learning classifier, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 August 2006, Proceedings pp: 2886-2891.（EI）

[37]

Xizhao Wang(*), Feng Yang, Yan Li, A discussion on the overlapping in fuzzy production rule reasoning, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 August 2006, Proceedings pp: 4557-4562.（EI）

[36]

Xizhao Wang(*), Xuguang Wang and Jun Shen, The representation of interaction among fuzzy rules, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 3098-3103.（EI）

Xizhao Wang(*), Yan Ha and Degang Chen, On the reduction of fuzzy rough sets, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 3174-3178.（EI）

[33]

Xizhao Wang(*), Sufang Zhang and Junhai Zhai, A nonlinear integral defined on partition of set and its fundamental properties, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 3092-3097.（EI）

Xizhao Wang(*), Chunguo Li, A new definition of sensitivity for RBFNN and its applications to feature reduction, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, 18-21 August 2005, Proceedings pp: 81-86.（EI）

Xizhao Wang(*), Xiaojun Wang, A new methodology for determining fuzzy densities in the fusion model based on fuzzy integral, Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, China, 26-29, August, 2004, Proceedings pp: 2028-2031.（EI）

Qiang He(*), Xizhao Wang, Hongjie Xing, A fuzzy classification method based on support vector machine, Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003,Proceedings pp: 1237-1240.（EI）

[16]

Qunfeng Zhang(*), Xizhao Wang, Jinghong Wang, A further study on simplification of decision tables, Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an, China, 2-5 November 2003,Proceedings pp: 1657-1661.（EI）

D. S. Yeung(*), Xizhao Wang, Using a neuro-fuzzy technique to improve the clustering based on similarity, in Proceedings of IEEE International Conference on IEEE International Conference on Systems, Man, and Cybernetics, Nashville, Tennessee, USA, 8-11 October 2000, Proceedings pp: 3693-3698.（EI）

When & Where

About Speaker

XizhaoWang, PhD, Professor

IEEE Fellow, CAAI Fellow

JMLC Editor-in-Chief

Big Data Institute, ShenZhen Univ.

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SOME DETAILS ABOUT THIS TUTORIAL

Mainly Content

In order to find true happiness, we first must learn to change our attitudes about money. We must learn that money, and the spending of it, provides only a temporary relief but does not present us with any real long lasting benefits. We end up owning something we either do not really want or do not really need, and the underlying emotional issues remain.

When & Where

In room 230, we will start at 10 o'clock am

How long it will take

Around 2 hours

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ABOUT THIS RECRUITMENT

The city of Shenzhen is a well-known special economic zone in China, which is adjacent to the international metropolis of Hong Kong and Macao. It is one of the most developed, most modern and internationalized regions in China. Shenzhen University is one of the fast developing universities in China with its distinctive features, huge potential and great popularity. It is now accelerating its steps to reach the goal of becoming a top modern university. Shenzhen University is actively recruiting foreign students to pursue their doctoral degrees.

Study Form and Duration

1.Full-time study form.

2.The normal duration is 3 years, with the maximum of 5 years. The total required time for curriculum study and DOIng research in the school shall not be less than one and a half years.

Application Time and Method

Register online via the website of the College of International Exchange (http://lxs.szu.edu.cn/) and upload application materials as required. The application time is from March 20th to April 30th each year.